* using log directory ‘/data/gannet/ripley/R/packages/tests-noLD/glmmrBase.Rcheck’
* using R Under development (unstable) (2026-03-31 r89756)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7)
    GNU Fortran (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7)
* running under: Fedora Linux 42 (Workstation Edition)
* using session charset: UTF-8
* current time: 2026-04-01 03:04:38 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘glmmrBase/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘glmmrBase’ version ‘1.3.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
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* checking for sufficient/correct file permissions ... OK
* checking whether package ‘glmmrBase’ can be installed ... [13m/15m] OK
* used C++ compiler: ‘g++ (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7)’
* checking C++ specification ... INFO
  specified C++17
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
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* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [23s/22s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... INFO
GNU make is a SystemRequirements.
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking examples ... ERROR
Running examples in ‘glmmrBase-Ex.R’ failed
The error most likely occurred in:

> ### Name: Model
> ### Title: A GLMM Model
> ### Aliases: Model
> 
> ### ** Examples
> 
> 
> ## ------------------------------------------------
> ## Method `Model$new`
> ## ------------------------------------------------
> 
> ## Don't show: 
> setParallel(FALSE) 
> ## End(Don't show)
> # For more examples, see the examples for MCML.
> 
> #create a data frame describing a cross-sectional parallel cluster
> #randomised trial
> df <- nelder(~(cl(10)*t(5)) > ind(10))
> df$int <- 0
> df[df$cl > 5, 'int'] <- 1
> mod <- Model$new(
+   formula = ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)),
+   data = df,
+   family = stats::gaussian()
+ )
> 
> # We can also include the outcome data in the model initialisation. 
> # For example, simulating data and creating a new object:
> df$y <- mod$sim_data()
> 
> mod <- Model$new(
+   formula = y ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)),
+   data = df,
+   family = stats::gaussian()
+ )
> 
> # Here we will specify a cohort study
> df <- nelder(~ind(20) * t(6))
> df$int <- 0
> df[df$t > 3, 'int'] <- 1
> 
> des <- Model$new(
+   formula = ~ int + (1|gr(ind)),
+   data = df,
+   family = stats::poisson()
+ )
>   
> # or with parameter values specified
>   
> des <- Model$new(
+   formula = ~ int + (1|gr(ind)),
+   covariance = c(0.05),
+   mean = c(1,0.5),
+   data = df,
+   family = stats::poisson()
+ )
> 
> #an example of a spatial grid with two time points
> 
> df <- nelder(~ (x(10)*y(10))*t(2))
> spt_design <- Model$new(formula = ~ 1 + (1|ar0(t)*fexp(x,y)),
+                         data = df,
+                         family = stats::gaussian())
> 
> ## ------------------------------------------------
> ## Method `Model$sim_data`
> ## ------------------------------------------------
> 
> df <- nelder(~(cl(10)*t(5)) > ind(10))
> df$int <- 0
> df[df$cl > 5, 'int'] <- 1
> ## Don't show: 
> setParallel(FALSE) # for the CRAN check
> ## End(Don't show)
> des <- Model$new(
+   formula = ~ factor(t) + int - 1 + (1|gr(cl)*ar0(t)),
+   covariance = c(0.05,0.8),
+   mean = c(rep(0,5),0.6),
+   data = df,
+   family = stats::binomial()
+ )
> ysim <- des$sim_data()
> 
> ## ------------------------------------------------
> ## Method `Model$update_parameters`
> ## ------------------------------------------------
> 
> ## Don't show: 
> setParallel(FALSE) # for the CRAN check
> ## End(Don't show)
> df <- nelder(~(cl(10)*t(5)) > ind(10))
> df$int <- 0
> df[df$cl > 5, 'int'] <- 1
> des <- Model$new(
+   formula = ~ factor(t) + int - 1 + (1|gr(cl)*ar0(t)),
+   data = df,
+   family = stats::binomial()
+ )
> des$update_parameters(cov.pars = c(0.1,0.9))
> 
> ## ------------------------------------------------
> ## Method `Model$power`
> ## ------------------------------------------------
> 
> ## Don't show: 
> setParallel(FALSE) # for the CRAN check
> ## End(Don't show)
> df <- nelder(~(cl(10)*t(5)) > ind(10))
> df$int <- 0
> df[df$cl > 5, 'int'] <- 1
> des <- Model$new(
+   formula = ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)),
+   covariance = c(0.05,0.1),
+   mean = c(rep(0,5),0.6),
+   data = df,
+   family = stats::gaussian(),
+   var_par = 1
+ )
> des$power() #power of 0.90 for the int parameter
      Value        SE    Power
b_t1    0.0 0.1843909 0.025000
b_t2    0.0 0.1843909 0.025000
b_t3    0.0 0.1843909 0.025000
b_t4    0.0 0.1843909 0.025000
b_t5    0.0 0.1843909 0.025000
b_int   0.6 0.1897367 0.885379
> 
> ## ------------------------------------------------
> ## Method `Model$MCML`
> ## ------------------------------------------------
> 
> ## Not run: 
> ##D # Simulated trial data example
> ##D data(SimTrial,package = "glmmrBase")
> ##D model <- Model$new(
> ##D   formula = y ~ int + factor(t) - 1 + (1|gr(cl)*ar1(t)),
> ##D   data = SimTrial,
> ##D   family = gaussian()
> ##D )
> ##D glm3 <- model$MCML()
> ##D 
> ##D # Salamanders data example
> ##D data(Salamanders,package="glmmrBase")
> ##D model <- Model$new(
> ##D   mating~fpop:mpop-1+(1|gr(mnum))+(1|gr(fnum)),
> ##D   data = Salamanders,
> ##D   family = binomial()
> ##D )
> ##D 
> ##D # use MCEM + REML with 500 sampling iterations
> ##D glm2 <- model$MCML(method = "mcem", iter.sampling = 500, reml = TRUE)
> ##D 
> ##D # as an alternative, we will specify the variance parameters on the 
> ##D # log scale and use a fast fitting algorithm
> ##D # we will use two newton-raphson steps, and Normal approximation posteriors with 
> ##D # conjugate gradient descent
> ##D # the maximum number of iterations is increased as it takes 100-110 in this example
> ##D # we can also chain together the functions
> ##D glm3 <- Model$new(
> ##D   mating~fpop:mpop-1+(1|grlog(mnum))+(1|grlog(fnum)),
> ##D   data = Salamanders,
> ##D   family = binomial()
> ##D )$MCML(method = "mcnr2", mcmc.pkg = "analytic", iter.sampling = 50, max.iter = 150)
> ##D 
> ##D # Example using simulated data
> ##D #create example data with six clusters, five time periods, and five people per cluster-period
> ##D df <- nelder(~(cl(6)*t(5)) > ind(5))
> ##D # parallel trial design intervention indicator
> ##D df$int <- 0
> ##D df[df$cl > 3, 'int'] <- 1 
> ##D # specify parameter values in the call for the data simulation below
> ##D des <- Model$new(
> ##D   formula= ~ factor(t) + int - 1 +(1|gr(cl)*ar0(t)),
> ##D   covariance = c(0.05,0.7),
> ##D   mean = c(rep(0,5),0.2),
> ##D   data = df,
> ##D   family = gaussian()
> ##D )
> ##D ysim <- des$sim_data() # simulate some data from the model
> ##D fit1 <- des$MCML(y = ysim) # Default model fitting with SAEM
> ##D # use MCNR instead and stop when parameter values are within 1e-2 on successive iterations
> ##D fit2 <- des$MCML(y = ysim, method="mcnr",tol=1e-2,conv.criterion = 1)
> ##D 
> ##D # Non-linear model fitting example using the example provided by nlmer in lme4
> ##D data(Orange, package = "lme4")
> ##D 
> ##D # the lme4 example:
> ##D startvec <- c(Asym = 200, xmid = 725, scal = 350)
> ##D (nm1 <- lme4::nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
> ##D               Orange, start = startvec))
> ##D 
> ##D Orange <- as.data.frame(Orange)
> ##D Orange$Tree <- as.numeric(Orange$Tree)
> ##D 
> ##D # Here we can specify the model as a function. 
> ##D 
> ##D model <- Model$new(
> ##D   circumference ~ Asym/(1 + exp((xmid - (age))/scal)) - 1 + (Asym|gr(Tree)),
> ##D   data = Orange,
> ##D   family = gaussian(),
> ##D   mean = c(200,725,350),
> ##D   covariance = c(500),
> ##D   var_par = 50
> ##D )
> ##D 
> ##D # for this example, we will use MCEM with adaptive MCMC sample sizes
> ##D 
> ##D nfit <- model$MCML(method = "mcem.adapt", iter.sampling = 1000)
> ##D 
> ##D summary(nfit)
> ##D summary(nm1)
> ##D 
> ##D 
> ## End(Not run)
> 
> ## ------------------------------------------------
> ## Method `Model$fit`
> ## ------------------------------------------------
> 
> # Simulated trial data example using REML
> data(SimTrial,package = "glmmrBase")
> fit1 <- Model$new(
+   formula = y ~ int + factor(t) - 1 + (1|grlog(cl)*ar0log(t)),
+   data = SimTrial,
+   family = gaussian()
+ )$fit(reml = TRUE)
> 
> # Salamanders data example
> data(Salamanders,package="glmmrBase")
> model <- Model$new(
+   mating~fpop:mpop-1+(1|grlog(mnum))+(1|grlog(fnum)),
+   data = Salamanders,
+   family = binomial()
+ )
> 
> fit2 <- model$fit()
ERROR: beta[0] is NaN/Inf: -nan
ERROR: beta[1] is NaN/Inf: -nan
ERROR: beta[2] is NaN/Inf: -nan
ERROR: beta[3] is NaN/Inf: -nan
ERROR: u_solve_ contains NaN
ERROR: u_weight_ contains NaN/Inf

=== CONTEXT (from beta step) ===
Dimensions: n=120, p=4, Q=20
beta: -nan -nan -nan -nan 
theta: -0.150688 -41773.4 
y range: [0, 1]
offset range: [0, 0]
u_ range: [-3.22341, 3.48768]
u_mean_ range: [-1.60453, 1.34919]
u_weight_ sum: -nan, ESS: -nan
Error: Numerical error detected. See diagnostics above.
Execution halted
* checking PDF version of manual ... OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/gannet/ripley/R/packages/tests-noLD/glmmrBase.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 17:17.97, 896.37 + 48.96